# UNHCR Demographics
# Author: Connor Kelly
# Date: December 1, 2020
library(tidyverse)
library(plotly)
setwd("C:/Users/Connor/Documents/GitHub/MDI")
# Import Data
agesex <- read_csv("Data/unhcr_popstats_export_demographics_all_data.csv",
skip = 3) # Date extracted: 2015-09-18 04:37:44 +02:00 (update?) ## Warning: 48820 parsing failures.
## row col expected actual file
## 3230 Female 5-11 1/0/T/F/TRUE/FALSE 88 'Data/unhcr_popstats_export_demographics_all_data.csv'
## 3230 Female 12-17 1/0/T/F/TRUE/FALSE 45 'Data/unhcr_popstats_export_demographics_all_data.csv'
## 3230 Male 5-11 1/0/T/F/TRUE/FALSE 115 'Data/unhcr_popstats_export_demographics_all_data.csv'
## 3230 Male 12-17 1/0/T/F/TRUE/FALSE 38 'Data/unhcr_popstats_export_demographics_all_data.csv'
## 3231 Female 5-11 1/0/T/F/TRUE/FALSE 139 'Data/unhcr_popstats_export_demographics_all_data.csv'
## .... ............ .................. ...... ......................................................
## See problems(...) for more details.# Examine Data
agesex[is.na(agesex)] <- 0 # Replace missing values as 0
str(agesex) # Both Male and Female 5-11 and 12-17 are logical rather than numeric## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 19869 obs. of 19 variables:
## $ Year : num 2001 2001 2001 2001 2001 ...
## $ Country / territory of asylum/residence: chr "Afghanistan" "Afghanistan" "Afghanistan" "Angola" ...
## $ Location Name : chr "Kabul" "Various" "Herat" "Viana" ...
## $ Female 0-4 : num 0 14335 0 484 219 ...
## $ Female 5-11 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Female 5-17 : num 1 45451 0 1687 734 ...
## $ Female 12-17 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Female 18-59 : num 1 99880 1 1282 427 ...
## $ Female 60+ : num 0 19234 0 43 25 ...
## $ F: Unknown : num 0 412004 0 0 0 ...
## $ F: Total : num 2 590904 1 3496 1405 ...
## $ Male 0-4 : num 0 14716 0 597 226 ...
## $ Male 5-11 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Male 5-17 : num 0 47522 0 1645 711 ...
## $ Male 12-17 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Male 18-59 : num 2 114965 1 787 139 ...
## $ Male 60+ : num 0 13025 0 34 15 ...
## $ M: Unknown : num 0 435492 0 0 0 ...
## $ M: Total : num 2 625720 1 3063 1091 ...
## - attr(*, "problems")=Classes 'tbl_df', 'tbl' and 'data.frame': 48820 obs. of 5 variables:
## ..$ row : int 3230 3230 3230 3230 3231 3231 3231 3231 3232 3232 ...
## ..$ col : chr "Female 5-11" "Female 12-17" "Male 5-11" "Male 12-17" ...
## ..$ expected: chr "1/0/T/F/TRUE/FALSE" "1/0/T/F/TRUE/FALSE" "1/0/T/F/TRUE/FALSE" "1/0/T/F/TRUE/FALSE" ...
## ..$ actual : chr "88" "45" "115" "38" ...
## ..$ file : chr "'Data/unhcr_popstats_export_demographics_all_data.csv'" "'Data/unhcr_popstats_export_demographics_all_data.csv'" "'Data/unhcr_popstats_export_demographics_all_data.csv'" "'Data/unhcr_popstats_export_demographics_all_data.csv'" ...
## - attr(*, "spec")=
## .. cols(
## .. Year = col_double(),
## .. `Country / territory of asylum/residence` = col_character(),
## .. `Location Name` = col_character(),
## .. `Female 0-4` = col_double(),
## .. `Female 5-11` = col_logical(),
## .. `Female 5-17` = col_double(),
## .. `Female 12-17` = col_logical(),
## .. `Female 18-59` = col_double(),
## .. `Female 60+` = col_double(),
## .. `F: Unknown` = col_double(),
## .. `F: Total` = col_double(),
## .. `Male 0-4` = col_double(),
## .. `Male 5-11` = col_logical(),
## .. `Male 5-17` = col_double(),
## .. `Male 12-17` = col_logical(),
## .. `Male 18-59` = col_double(),
## .. `Male 60+` = col_double(),
## .. `M: Unknown` = col_double(),
## .. `M: Total` = col_double()
## .. )agesex = subset(agesex, select=-c(`Female 5-11`, `Female 12-17`, `F: Total`, `Male 5-11`, `Male 12-17`, `M: Total`)) # Drop logical columns and totals
# Aggregate by country of asylum
agesex <- agesex %>%
group_by(Year, `Country / territory of asylum/residence`) %>%
summarize_if(is.numeric, sum)
# Establish color palette for plots
colors <- c( "#FB6A4A", "#EF3B2C", "#CB181D", "#A50F15", "#67000D", # red palette (female)
"#6BAED6", "#4292C6", "#2171B5", "#08519C", "#08306B") # blue palette (male)This data shows the age and sex disaggregated breakdown of refugees and asylum seekers residing in the given countries of asylum. It is important to note that this data focuses on the country of residence of refugees and asylum seekers, not their country of origin.
afg <- agesex %>% filter(`Country / territory of asylum/residence`=="Afghanistan")
afg <- pivot_longer(afg,
cols = c(3:12),
names_to = "agesex",
values_to = "refugees")
afg$agesex <- factor(afg$agesex, levels = c("Female 0-4", "Female 5-17", "Female 18-59", "Female 60+", "F: Unknown",
"Male 0-4", "Male 5-17", "Male 18-59", "Male 60+", "M: Unknown"))
afg_p <- ggplot(afg, aes(x=Year, y=refugees, fill=agesex)) + geom_area() + scale_fill_manual(values = colors) +
ggtitle("Age and Sex of Displaced in Afghanistan") + ylab("Refugees and Asylum Seekers") + theme(legend.title = element_blank())
ggplotly(afg_p, dynamicTicks = TRUE) %>%
layout(hovermode = "x")bgd <- agesex %>% filter(`Country / territory of asylum/residence`=="Bangladesh")
bgd <- pivot_longer(bgd,
cols = c(3:12),
names_to = "agesex",
values_to = "refugees")
bgd$agesex <- factor(bgd$agesex, levels = c("Female 0-4", "Female 5-17", "Female 18-59", "Female 60+", "F: Unknown",
"Male 0-4", "Male 5-17", "Male 18-59", "Male 60+", "M: Unknown"))
bgd_p <- ggplot(bgd, aes(x=Year, y=refugees, fill=agesex)) + geom_area() + scale_fill_manual(values = colors) +
ggtitle("Age and Sex of Displaced in Bangladesh") + ylab("Refugees and Asylum Seekers") + theme(legend.title = element_blank())
ggplotly(bgd_p, dynamicTicks = TRUE) %>%
layout(hovermode = "x")bdi <- agesex %>% filter(`Country / territory of asylum/residence`=="Burundi")
bdi <- pivot_longer(bdi,
cols = c(3:12),
names_to = "agesex",
values_to = "refugees")
bdi$agesex <- factor(bdi$agesex, levels = c("Female 0-4", "Female 5-17", "Female 18-59", "Female 60+", "F: Unknown",
"Male 0-4", "Male 5-17", "Male 18-59", "Male 60+", "M: Unknown"))
bdi_p <- ggplot(bdi, aes(x=Year, y=refugees, fill=agesex)) + geom_area() + scale_fill_manual(values = colors) +
ggtitle("Age and Sex of Displaced in Burundi") + ylab("Refugees and Asylum Seekers") + theme(legend.title = element_blank())
ggplotly(bdi_p, dynamicTicks = TRUE) %>%
layout(hovermode = "x")caf <- agesex %>% filter(`Country / territory of asylum/residence`=="Central African Rep.")
caf <- pivot_longer(caf,
cols = c(3:12),
names_to = "agesex",
values_to = "refugees")
caf$agesex <- factor(caf$agesex, levels = c("Female 0-4", "Female 5-17", "Female 18-59", "Female 60+", "F: Unknown",
"Male 0-4", "Male 5-17", "Male 18-59", "Male 60+", "M: Unknown"))
caf_p <- ggplot(bdi, aes(x=Year, y=refugees, fill=agesex)) + geom_area() + scale_fill_manual(values = colors) +
ggtitle("Age and Sex of Displaced in Central African Republic") + ylab("Refugees and Asylum Seekers") + theme(legend.title = element_blank())
ggplotly(caf_p, dynamicTicks = TRUE) %>%
layout(hovermode = "x")tcd <- agesex %>% filter(`Country / territory of asylum/residence`=="Chad")
tcd <- pivot_longer(tcd,
cols = c(3:12),
names_to = "agesex",
values_to = "refugees")
tcd$agesex <- factor(tcd$agesex, levels = c("Female 0-4", "Female 5-17", "Female 18-59", "Female 60+", "F: Unknown",
"Male 0-4", "Male 5-17", "Male 18-59", "Male 60+", "M: Unknown"))
tcd_p <- ggplot(tcd, aes(x=Year, y=refugees, fill=agesex)) + geom_area() + scale_fill_manual(values = colors) +
ggtitle("Age and Sex of Displaced in Chad") + ylab("Refugees and Asylum Seekers") + theme(legend.title = element_blank())
ggplotly(tcd_p, dynamicTicks = TRUE) %>%
layout(hovermode = "x")eth <- agesex %>% filter(`Country / territory of asylum/residence`=="Ethiopia")
eth <- pivot_longer(eth,
cols = c(3:12),
names_to = "agesex",
values_to = "refugees")
eth$agesex <- factor(eth$agesex, levels = c("Female 0-4", "Female 5-17", "Female 18-59", "Female 60+", "F: Unknown",
"Male 0-4", "Male 5-17", "Male 18-59", "Male 60+", "M: Unknown"))
eth_p <- ggplot(eth, aes(x=Year, y=refugees, fill=agesex)) + geom_area() + scale_fill_manual(values = colors) +
ggtitle("Age and Sex of Displaced in Ethiopia") + ylab("Refugees and Asylum Seekers") + theme(legend.title = element_blank())
ggplotly(eth_p, dynamicTicks = TRUE) %>%
layout(hovermode = "x")gtm <- agesex %>% filter(`Country / territory of asylum/residence`=="Guatemala")
gtm <- pivot_longer(gtm,
cols = c(3:12),
names_to = "agesex",
values_to = "refugees")
gtm$agesex <- factor(gtm$agesex, levels = c("Female 0-4", "Female 5-17", "Female 18-59", "Female 60+", "F: Unknown",
"Male 0-4", "Male 5-17", "Male 18-59", "Male 60+", "M: Unknown"))
gtm_p <- ggplot(gtm, aes(x=Year, y=refugees, fill=agesex)) + geom_area() + scale_fill_manual(values = colors) +
ggtitle("Age and Sex of Displaced in Guatemala") + ylab("Refugees and Asylum Seekers") + theme(legend.title = element_blank())
ggplotly(gtm_p, dynamicTicks = TRUE) %>%
layout(hovermode = "x")irq <- agesex %>% filter(`Country / territory of asylum/residence`=="Iraq")
irq <- pivot_longer(irq,
cols = c(3:12),
names_to = "agesex",
values_to = "refugees")
irq$agesex <- factor(irq$agesex, levels = c("Female 0-4", "Female 5-17", "Female 18-59", "Female 60+", "F: Unknown",
"Male 0-4", "Male 5-17", "Male 18-59", "Male 60+", "M: Unknown"))
irq_p <- ggplot(irq, aes(x=Year, y=refugees, fill=agesex)) + geom_area() + scale_fill_manual(values = colors) +
ggtitle("Age and Sex of Displaced in Iraq") + ylab("Refugees and Asylum Seekers") + theme(legend.title = element_blank())
ggplotly(irq_p, dynamicTicks = TRUE) %>%
layout(hovermode = "x")lby <- agesex %>% filter(`Country / territory of asylum/residence`=="Libya")
lby <- pivot_longer(lby,
cols = c(3:12),
names_to = "agesex",
values_to = "refugees")
lby$agesex <- factor(lby$agesex, levels = c("Female 0-4", "Female 5-17", "Female 18-59", "Female 60+", "F: Unknown",
"Male 0-4", "Male 5-17", "Male 18-59", "Male 60+", "M: Unknown"))
lby_p <- ggplot(lby, aes(x=Year, y=refugees, fill=agesex)) + geom_area() + scale_fill_manual(values = colors) +
ggtitle("Age and Sex of Displaced in Lybia") + ylab("Refugees and Asylum Seekers") + theme(legend.title = element_blank())
ggplotly(lby_p, dynamicTicks = TRUE) %>%
layout(hovermode = "x")mli <- agesex %>% filter(`Country / territory of asylum/residence`=="Mali")
mli <- pivot_longer(mli,
cols = c(3:12),
names_to = "agesex",
values_to = "refugees")
mli$agesex <- factor(mli$agesex, levels = c("Female 0-4", "Female 5-17", "Female 18-59", "Female 60+", "F: Unknown",
"Male 0-4", "Male 5-17", "Male 18-59", "Male 60+", "M: Unknown"))
mli_p <- ggplot(mli, aes(x=Year, y=refugees, fill=agesex)) + geom_area() + scale_fill_manual(values = colors) +
ggtitle("Age and Sex of Displaced in Mali") + ylab("Refugees and Asylum Seekers") + theme(legend.title = element_blank())
ggplotly(mli_p, dynamicTicks = TRUE) %>%
layout(hovermode = "x")mmr <- agesex %>% filter(`Country / territory of asylum/residence`=="Myanmar")
mmr <- pivot_longer(mmr,
cols = c(3:12),
names_to = "agesex",
values_to = "refugees")
mmr$agesex <- factor(mmr$agesex, levels = c("Female 0-4", "Female 5-17", "Female 18-59", "Female 60+", "F: Unknown",
"Male 0-4", "Male 5-17", "Male 18-59", "Male 60+", "M: Unknown"))
mmr_p <- ggplot(mmr, aes(x=Year, y=refugees, fill=agesex)) + geom_area() + scale_fill_manual(values = colors) +
ggtitle("Age and Sex of Displaced in Myanmar") + ylab("Refugees and Asylum Seekers") + theme(legend.title = element_blank())
ggplotly(mmr_p, dynamicTicks = TRUE) %>%
layout(hovermode = "x")nga <- agesex %>% filter(`Country / territory of asylum/residence`=="Nigeria")
nga <- pivot_longer(nga,
cols = c(3:12),
names_to = "agesex",
values_to = "refugees")
nga$agesex <- factor(nga$agesex, levels = c("Female 0-4", "Female 5-17", "Female 18-59", "Female 60+", "F: Unknown",
"Male 0-4", "Male 5-17", "Male 18-59", "Male 60+", "M: Unknown"))
nga_p <- ggplot(nga, aes(x=Year, y=refugees, fill=agesex)) + geom_area() + scale_fill_manual(values = colors) +
ggtitle("Age and Sex of Displaced in Nigeria") + ylab("Refugees and Asylum Seekers") + theme(legend.title = element_blank())
ggplotly(nga_p, dynamicTicks = TRUE) %>%
layout(hovermode = "x")syr <- agesex %>% filter(`Country / territory of asylum/residence`=="Syrian Arab Rep.")
syr <- pivot_longer(syr,
cols = c(3:12),
names_to = "agesex",
values_to = "refugees")
syr$agesex <- factor(syr$agesex, levels = c("Female 0-4", "Female 5-17", "Female 18-59", "Female 60+", "F: Unknown",
"Male 0-4", "Male 5-17", "Male 18-59", "Male 60+", "M: Unknown"))
syr_p <- ggplot(syr, aes(x=Year, y=refugees, fill=agesex)) + geom_area() + scale_fill_manual(values = colors) +
ggtitle("Age and Sex of Displaced in Syria") + ylab("Refugees and Asylum Seekers") + theme(legend.title = element_blank())
ggplotly(syr_p, dynamicTicks = TRUE) %>%
layout(hovermode = "x")sdn <- agesex %>% filter(`Country / territory of asylum/residence`=="Sudan")
sdn <- pivot_longer(sdn,
cols = c(3:12),
names_to = "agesex",
values_to = "refugees")
sdn$agesex <- factor(sdn$agesex, levels = c("Female 0-4", "Female 5-17", "Female 18-59", "Female 60+", "F: Unknown",
"Male 0-4", "Male 5-17", "Male 18-59", "Male 60+", "M: Unknown"))
sdn_p <- ggplot(sdn, aes(x=Year, y=refugees, fill=agesex)) + geom_area() + scale_fill_manual(values = colors) +
ggtitle("Age and Sex of Displaced in Sudan") + ylab("Refugees and Asylum Seekers") + theme(legend.title = element_blank())
ggplotly(sdn_p, dynamicTicks = TRUE) %>%
layout(hovermode = "x")ssd <- agesex %>% filter(`Country / territory of asylum/residence`=="South Sudan")
ssd <- pivot_longer(ssd,
cols = c(3:12),
names_to = "agesex",
values_to = "refugees")
ssd$agesex <- factor(ssd$agesex, levels = c("Female 0-4", "Female 5-17", "Female 18-59", "Female 60+", "F: Unknown",
"Male 0-4", "Male 5-17", "Male 18-59", "Male 60+", "M: Unknown"))
ssd_p <- ggplot(ssd, aes(x=Year, y=refugees, fill=agesex)) + geom_area() + scale_fill_manual(values = colors) +
ggtitle("Age and Sex of Displaced in South Sudan") + ylab("Refugees and Asylum Seekers") + theme(legend.title = element_blank())
ggplotly(ssd_p, dynamicTicks = TRUE) %>%
layout(hovermode = "x")som <- agesex %>% filter(`Country / territory of asylum/residence`=="Somalia")
som <- pivot_longer(som,
cols = c(3:12),
names_to = "agesex",
values_to = "refugees")
som$agesex <- factor(som$agesex, levels = c("Female 0-4", "Female 5-17", "Female 18-59", "Female 60+", "F: Unknown",
"Male 0-4", "Male 5-17", "Male 18-59", "Male 60+", "M: Unknown"))
som_p <- ggplot(som, aes(x=Year, y=refugees, fill=agesex)) + geom_area() + scale_fill_manual(values = colors) +
ggtitle("Age and Sex of Displaced in Somalia") + ylab("Refugees and Asylum Seekers") + theme(legend.title = element_blank())
ggplotly(som_p, dynamicTicks = TRUE) %>%
layout(hovermode = "x")ven <- agesex %>% filter(`Country / territory of asylum/residence`=="Venezuela (Bolivarian Republic of)")
ven <- pivot_longer(ven,
cols = c(3:12),
names_to = "agesex",
values_to = "refugees")
ven$agesex <- factor(ven$agesex, levels = c("Female 0-4", "Female 5-17", "Female 18-59", "Female 60+", "F: Unknown",
"Male 0-4", "Male 5-17", "Male 18-59", "Male 60+", "M: Unknown"))
ven_p <- ggplot(ven, aes(x=Year, y=refugees, fill=agesex)) + geom_area() + scale_fill_manual(values = colors) +
ggtitle("Age and Sex of Displaced in Venezuela") + ylab("Refugees and Asylum Seekers") + theme(legend.title = element_blank())
ggplotly(ven_p, dynamicTicks = TRUE) %>%
layout(hovermode = "x")yem <- agesex %>% filter(`Country / territory of asylum/residence`=="Yemen")
yem <- pivot_longer(yem,
cols = c(3:12),
names_to = "agesex",
values_to = "refugees")
yem$agesex <- factor(yem$agesex, levels = c("Female 0-4", "Female 5-17", "Female 18-59", "Female 60+", "F: Unknown",
"Male 0-4", "Male 5-17", "Male 18-59", "Male 60+", "M: Unknown"))
yem_p <- ggplot(yem, aes(x=Year, y=refugees, fill=agesex)) + geom_area() + scale_fill_manual(values = colors) +
ggtitle("Age and Sex of Displaced in Yemen") + ylab("Refugees and Asylum Seekers") + theme(legend.title = element_blank())
ggplotly(yem_p, dynamicTicks = TRUE) %>%
layout(hovermode = "x")